Hybrid-Granularity Parallelism Support for Fast Transaction Processing in Blockchain-Based Federated Learning
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| I publikationen: | The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025), p. 616-628 |
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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| Abstrakt: | Conference Title: 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS)Conference Start Date: 2025 June 3Conference End Date: 2025 June 7Conference Location: Milano, ItalyBlockchain-based Federated Learning (BCFL) is widely recognized as a promising solution for collaboratively training machine learning models while maintaining system security. Since blockchain systems are transaction-driven, the efficiency of transaction processing is directly related to the performance and availability of the BCFL system. Previous research has primarily focused on optimizing storage mechanisms or integrating Trusted Execution Environment (TEE) to reduce transaction processing pressure. However, the performance of BCFL remains constrained by slow transaction processing. This critical bottleneck arises from scalar instruction operations in transaction execution engines and the inherent serial transaction processing mechanism. In this paper, we propose a novel hybrid-granularity parallelism architecture, HGP, to greatly accelerate transaction processing in BCFL systems. HGP achieves this through three major innovations: (1) a suite of extended vector instructions, which reduces the instruction number and execution latency by enabling vectorized data I/O and computation using very long instruction word (VLIW) techniques, (2) the scalable transaction grouping method that generates parallelizable transaction groups through transaction signature verification and read-write conflict detection, and (3) the multi-EVM (Ethereum Virtual Machine) parallel processing mechanism that processes a group of transactions using multiple execution engine threads, and maintains global consistency through group scheduling. Through these optimizations, HGP accelerates the transaction processing with both data-level and thread-level parallelism. We evaluate HGP by executing BCFL tasks over classic ResNet18, MobileNet, and SqueezeNet. The experimental results demonstrate that HGP achieves up to a $3.8 \times$ improvement in CPU utilization and a $1.6 \times$ improvement in memory utilization. Furthermore, HGP significantly speeds up the transaction processing performance of three critical tasks by up to $24.5 \times, 12.4 \times$, and $2.8 \times$, respectively. |
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| DOI: | 10.1109/IPDPS64566.2025.00061 |
| Källa: | Science Database |